Biological particle analysis apparatuses are well known in the art. See for example U.S. Pat. Nos. 4,338,024 and 4,393,466, which are assigned to the present assignee. Such prior art machines use a computer having a stored fixed program to detect and to classify detected biological particles.
Standard decision theory that is used to sort biological particle images is well known, and tends to sort particles by classification in a serial fashion. More specifically, for a urine sample containing a plurality of particle types, the particle images are searched for one or more particle features unique to a single particle type, and those images are extracted. This process is repeated for other particles one particle type at a time. The problem with this methodology is that each particle type can exhibit a range of values for the searched for particle feature(s), and the range of values can overlap with those of other particle types. There is also the problem of artifacts, which are particle images that have no clinical significance, e.g. talc or hair, or cannot be classified due to the sensitivity of the imaging device or other problems with the image (e.g. boundary of particle undefined due to partial capture). Artifact particle images need to be disregarded from the analysis in such a way as to not adversely affect the overall accuracy of the particle analysis. Thus, it can be difficult to accurately but reliably classify particles in a sample containing artifacts.
Most biological particle classification devices further necessitate manual manipulation to accurately classify the particles in the sample. While particle features can be used to segregate particle images by particle type, a trained user is needed to verify the result.
Neural net computers are also well known. The advantage of a neural net computer is its ability to “learn” from its experiences, and thus a neural net computer, in theory, can become more intelligent as it is trained.
There is a need for a biological particle classification method and apparatus for accurate and automated classification of biological particles in a sample, such as a urine sample.